Who Pays? Personalization, Bossiness and the Cost of Fairness
- URL: http://arxiv.org/abs/2209.04043v1
- Date: Thu, 8 Sep 2022 21:47:10 GMT
- Title: Who Pays? Personalization, Bossiness and the Cost of Fairness
- Authors: Paresha Farastu, Nicholas Mattei and Robin Burke
- Abstract summary: Fairness-aware recommender systems that have a provider-side fairness concern seek to ensure that protected group(s) of providers have a fair opportunity to promote their items or products.
There is a cost of fairness'' borne by the consumer side of the interaction when such a solution is implemented.
This position paper introduces the concept of bossiness, shows its application in fairness-aware recommendation and discusses strategies for reducing this strategic incentive.
- Score: 24.75616876832476
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fairness-aware recommender systems that have a provider-side fairness concern
seek to ensure that protected group(s) of providers have a fair opportunity to
promote their items or products. There is a ``cost of fairness'' borne by the
consumer side of the interaction when such a solution is implemented. This
consumer-side cost raises its own questions of fairness, particularly when
personalization is used to control the impact of the fairness constraint. In
adopting a personalized approach to the fairness objective, researchers may be
opening their systems up to strategic behavior on the part of users. This type
of incentive has been studied in the computational social choice literature
under the terminology of ``bossiness''. The concern is that a bossy user may be
able to shift the cost of fairness to others, improving their own outcomes and
worsening those for others. This position paper introduces the concept of
bossiness, shows its application in fairness-aware recommendation and discusses
strategies for reducing this strategic incentive.
Related papers
- Fairness Dynamics in Digital Economy Platforms with Biased Ratings [50.29721091981893]
We study how digital platforms can perpetuate or counteract rating-based discrimination.<n>Our results demonstrate a fundamental trade-off between user experience and fairness.<n>Our results also provide evidence that intervening by tuning the demographics of the search results is a highly effective way of reducing unfairness.
arXiv Detail & Related papers (2026-02-18T18:41:16Z) - Personalized Pricing in Social Networks with Individual and Group Fairness Considerations [13.360754646928301]
This paper introduces a new formulation of the personalized pricing problem that incorporates both dimensions of fairness in social network settings.<n>We propose FairPricing, a novel framework that learns a personalized pricing policy using customer features and network topology.<n>Experiments show that FairPricing achieves high profitability while improving individual fairness perceptions and satisfying group fairness requirements.
arXiv Detail & Related papers (2025-12-12T03:20:29Z) - Fairness Incentives in Response to Unfair Dynamic Pricing [7.991187769447732]
We design a basic simulated economy, wherein we generate corporate taxation schedules geared to incentivizing firms towards adopting fair pricing behaviours.
To cover a range of possible policy scenarios, we formulate our social planner's learning problem as a multi-armed bandit, a contextual bandit and as a full reinforcement learning (RL) problem.
We find that social welfare improves on that of the fairness-agnostic baseline, and approaches that of the analytically optimal fairness-aware baseline for the multi-armed and contextual bandit settings.
arXiv Detail & Related papers (2024-04-22T23:12:58Z) - Incentivized Truthful Communication for Federated Bandits [61.759855777522255]
We propose an incentive compatible (i.e., truthful) communication protocol, named Truth-FedBan.
We show that Truth-FedBan still guarantees the sub-linear regret and communication cost without any overheads.
arXiv Detail & Related papers (2024-02-07T00:23:20Z) - Incentivized Communication for Federated Bandits [67.4682056391551]
We introduce an incentivized communication problem for federated bandits, where the server shall motivate clients to share data by providing incentives.
We propose the first incentivized communication protocol, namely, Inc-FedUCB, that achieves near-optimal regret with provable communication and incentive cost guarantees.
arXiv Detail & Related papers (2023-09-21T00:59:20Z) - Causal Fairness for Outcome Control [68.12191782657437]
We study a specific decision-making task called outcome control in which an automated system aims to optimize an outcome variable $Y$ while being fair and equitable.
In this paper, we first analyze through causal lenses the notion of benefit, which captures how much a specific individual would benefit from a positive decision.
We then note that the benefit itself may be influenced by the protected attribute, and propose causal tools which can be used to analyze this.
arXiv Detail & Related papers (2023-06-08T09:31:18Z) - A Survey on Fairness-aware Recommender Systems [59.23208133653637]
We present concepts of fairness in different recommendation scenarios, comprehensively categorize current advances, and introduce typical methods to promote fairness in different stages of recommender systems.
Next, we delve into the significant influence that fairness-aware recommender systems exert on real-world industrial applications.
arXiv Detail & Related papers (2023-06-01T07:08:22Z) - Joint Multisided Exposure Fairness for Recommendation [76.75990595228666]
This paper formalizes a family of exposure fairness metrics that model the problem jointly from the perspective of both the consumers and producers.
Specifically, we consider group attributes for both types of stakeholders to identify and mitigate fairness concerns that go beyond individual users and items towards more systemic biases in recommendation.
arXiv Detail & Related papers (2022-04-29T19:13:23Z) - Towards Fair Recommendation in Two-Sided Platforms [36.35034531426411]
We propose a fair personalized recommendation problem to a constrained version of the problem of fairly allocating indivisible goods.
Our proposed em FairRec algorithm guarantees Maxi-Min Share ($alpha$-MMS) of exposure for the producers, and Envy-Free up to One Item (EF1) fairness for the customers.
arXiv Detail & Related papers (2021-12-26T05:14:56Z) - Towards Personalized Fairness based on Causal Notion [18.5897206797918]
We introduce a framework for achieving counterfactually fair recommendations through adversary learning.
Our method can generate fairer recommendations for users with a desirable recommendation performance.
arXiv Detail & Related papers (2021-05-20T15:24:34Z) - Fairness, Welfare, and Equity in Personalized Pricing [88.9134799076718]
We study the interplay of fairness, welfare, and equity considerations in personalized pricing based on customer features.
We show the potential benefits of personalized pricing in two settings: pricing subsidies for an elective vaccine, and the effects of personalized interest rates on downstream outcomes in microcredit.
arXiv Detail & Related papers (2020-12-21T01:01:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.